This September 2018 paper introduces GraphSAGE, a novel inductive framework designed to generate node embeddings for large, evolving graphs, addressing limitations of prior transductive methods that struggle with unseen data. Instead of learning a specific embedding for each node, GraphSAGE learns a function that generates these embeddings by sampling and aggregating features from a node's local neighborhood. The authors evaluate various aggregator architectures, including mean, LSTM, and pooling functions, demonstrating that GraphSAGE significantly outperforms strong baselines on node classification tasks across diverse datasets, such as citation networks, Reddit posts, and protein-protein interaction graphs. The research also highlights GraphSAGE's computational efficiency and provides a theoretical analysis of its capability to learn local graph structural information, like clustering coefficients.
Source:
https://arxiv.org/pdf/1706.02216
